Heuristic search in restricted memory

Chakrabarti, P. P.; Ghose, Sujoy; Acharya, Amit; Sarkar, Sudeshna

doi:10.1016/0004-3702(89)90010-6

Cited by 84 publications

(42 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the simple successor ordering of Sort did not pay off, the other heuristics, namely PV, Trans and Trans+Move, reduce the search time by 13, 24 and 37%, respectively. These results compare favorably to those of others [25, Table 2], [2,12,21,22,29].…”

Section: Discussionsupporting

confidence: 87%

“…In our experience [19], hashing tables are much easier to implement and debug than the tree-structured data types of A* [3] and other IDA* variants [2,21,25]. In some way the transposition table plays a role similar to A*'s Open and Closed lists, with greater flexibility and speed, but with some risk of omission.…”

Section: Discussionmentioning

confidence: 99%

“…The repeated traversal of the explicit graph is the price paid for the missing Open List 2 . Even so, one would expect a graph 2 The repeated traversal of the explicit graph can be avoided by connecting the frontier nodes in a linked list, similar to A*'s Open list. But even then the savings would be negligible, because the list must be sorted before each new iteration.…”

Section: Related Limited-memory Algorithmsmentioning

confidence: 99%

“…Chakrabarti et al [2] proposed MA*, an iterative-deepening variant of Ibaraki's Depth-m Search [7]. Similar to MREC, MA* also grows an explicit search graph until the available memory space is filled, but dynamically re-assigns memory space to other states according to some merit value.…”

Section: Related Limited-memory Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced iterative-deepening search

Reinefeld

Marsland

1994

IEEE Trans. Pattern Anal. Machine Intell.

View full text Add to dashboard Cite

Iterative-deepening searches mimic a breadth-first node expansion with a series of depth-first searches that operate with successively extended search horizons. They have been proposed as a simple way to reduce the space complexity of best-first searches like A* from exponential to linear in the search depth.But there is more to iterative-deepening than just a reduction of storage space. As we show, the search efficiency can be greatly improved by exploiting previously gained node information. The information management techniques considered here owe much to their counterparts from the domain of two-player games, namely the use of fast-execution memory functions to guide the search. Our methods not only save node expansions, but are also faster and easier to implement than previous proposals.

show abstract

Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Related Limited-memory Algorithmsmentioning

confidence: 99%

Section: Related Limited-memory Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced iterative-deepening search

Reinefeld

Marsland

1994

IEEE Trans. Pattern Anal. Machine Intell.

View full text Add to dashboard Cite

show abstract

“…Thus this method does not involve complex computation and due to this reason cannot ensure the completeness of the solution. Hill climbing method does not give a solution as may terminate without reaching the goal state [12].Now let us look at algorithm of hill climbing for finding shortest path:…”

Section: Hill Climbing Methods For Shortest Path Findingmentioning

confidence: 99%

Comparison of Various Heuristic Search Techniques for Finding Shortest Path

Potdar¹,

Thool²

2014

IJAIA

View full text Add to dashboard Cite

show abstract

Stochastic node caching for memory-bounded search

Miura

Ishida

1999

Syst. Comp. Jpn.

View full text Add to dashboard Cite

With linear‐storage search, the same nodes are eventually revisited many times because only the search paths are stored to memory. Some algorithms such as MREC have been proposed to solve this problem by storing nodes as well. MREC is an algorithm that reduces the number of nodes revisited by storing a certain number of nodes located near the root node. Proposed in this paper is stochastic node caching, which involves storing nodes on a probability basis. In so doing, only those nodes are stored that are visited frequently, so that the number of nodes revisited can be reduced efficiently while using limited memory resources. To prove the efficiency of stochastic node caching, this method was compared with MREC while pursuing the same goal. Experiments were performed using the 15‐puzzle problem, a typical problem for linear‐storage search, and a more complicated problem of gene alignment. The experiments illustrated the properties of the two algorithms, and proved that stochastic node caching is efficient in reducing the number of nodes revisited. © 1999 Scripta Technica, Syst Comp Jpn, 30(2): 57–65, 1999

show abstract

Heuristic search in restricted memory

Cited by 84 publications

References 6 publications

Enhanced iterative-deepening search

Enhanced iterative-deepening search

Comparison of Various Heuristic Search Techniques for Finding Shortest Path

Stochastic node caching for memory-bounded search

Contact Info

Product

Resources

About